monitor model

A monitor model is a framework used in machine learning to evaluate how well a model predicts new, unseen data. It helps identify if the model is overfitting (performing well on training data but poorly elsewhere) or underfitting (failing to capture the underlying patterns). By using techniques like cross-validation—partitioning data into training and testing subsets—the monitor model estimates the model's generalization ability. This process ensures the model is robust and reliable, providing confidence that it will perform well in real-world scenarios.